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Big Data for Energy Management and Energy-Efficient Buildings

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  • Vangelis Marinakis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou str., 15773 Athens, Greece)

Abstract

European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.

Suggested Citation

  • Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1555-:d:337630
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    as
    1. Azadeh Sadeghi & Roohollah Younes Sinaki & William A. Young & Gary R. Weckman, 2020. "An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks," Energies, MDPI, vol. 13(3), pages 1-23, January.
    2. Giorgos Stamtsis & Haris Doukas, 2018. "Cooperation or Localization in European Capacity Markets? A Coalitional Game over Graph Approach," Energies, MDPI, vol. 11(6), pages 1-17, June.
    3. Doukas, Haris & Papadopoulou, Alexandra & Savvakis, Nikolaos & Tsoutsos, Theocharis & Psarras, John, 2012. "Assessing energy sustainability of rural communities using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1949-1957.
    4. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    5. Li, Y. & Kubicki, S. & Guerriero, A. & Rezgui, Y., 2019. "Review of building energy performance certification schemes towards future improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Tsoutsos, Theocharis & Drandaki, Maria & Frantzeskaki, Niki & Iosifidis, Eleftherios & Kiosses, Ioannis, 2009. "Sustainable energy planning by using multi-criteria analysis application in the island of Crete," Energy Policy, Elsevier, vol. 37(5), pages 1587-1600, May.
    7. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
    8. Doukas, Haris & Nikas, Alexandros, 2020. "Decision support models in climate policy," European Journal of Operational Research, Elsevier, vol. 280(1), pages 1-24.
    9. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    10. Bracco, Stefano & Delfino, Federico & Ferro, Giulio & Pagnini, Luisa & Robba, Michela & Rossi, Mansueto, 2018. "Energy planning of sustainable districts: Towards the exploitation of small size intermittent renewables in urban areas," Applied Energy, Elsevier, vol. 228(C), pages 2288-2297.
    11. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2020. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," Applied Energy, Elsevier, vol. 261(C).
    12. Heinermann, Justin & Kramer, Oliver, 2016. "Machine learning ensembles for wind power prediction," Renewable Energy, Elsevier, vol. 89(C), pages 671-679.
    13. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    14. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    15. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    16. Spiliotis, Evangelos & Kouloumos, Andreas & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Are forecasting competitions data representative of the reality?," International Journal of Forecasting, Elsevier, vol. 36(1), pages 37-53.
    17. Marinakis, Vangelis & Doukas, Haris & Xidonas, Panos & Zopounidis, Constantin, 2017. "Multicriteria decision support in local energy planning: An evaluation of alternative scenarios for the Sustainable Energy Action Plan," Omega, Elsevier, vol. 69(C), pages 1-16.
    18. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    19. Miller, Clayton & Nagy, Zoltán & Schlueter, Arno, 2018. "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1365-1377.
    20. Nikolaos Sifakis & Nikolaos Savvakis & Tryfon Daras & Theocharis Tsoutsos, 2019. "Analysis of the Energy Consumption Behavior of European RES Cooperative Members," Energies, MDPI, vol. 12(6), pages 1-19, March.
    21. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
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    7. Hernández, José L. & de Miguel, Ignacio & Vélez, Fredy & Vasallo, Ali, 2024. "Challenges and opportunities in European smart buildings energy management: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    8. Wadim Strielkowski & Anna Sherstobitova & Patrik Rovny & Tatiana Evteeva, 2021. "Increasing Energy Efficiency and Modernization of Energy Systems in Russia: A Review," Energies, MDPI, vol. 14(11), pages 1-19, May.
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    10. A-Ru-Han Bao & Yao Liu & Jun Dong & Zheng-Peng Chen & Zhen-Jie Chen & Chen Wu, 2022. "Evolutionary Game Analysis of Co-Opetition Strategy in Energy Big Data Ecosystem under Government Intervention," Energies, MDPI, vol. 15(6), pages 1-24, March.
    11. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    12. Elissaios Sarmas & Vangelis Marinakis & Haris Doukas, 2022. "A data-driven multicriteria decision making tool for assessing investments in energy efficiency," Operational Research, Springer, vol. 22(5), pages 5597-5616, November.
    13. Paweł Dymora & Mirosław Mazurek & Bartosz Sudek, 2021. "Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service," Energies, MDPI, vol. 14(22), pages 1-23, November.
    14. Jin, Keyan & Zhong, Ziqi & Zhao, Elena Yifei, 2024. "Sustainable digital marketing under big data: an AI random forest model approach," LSE Research Online Documents on Economics 121402, London School of Economics and Political Science, LSE Library.
    15. Alexey I. Shinkevich & Yuri Yu. Kostyukhin & Diana Yu. Savon & Andrey E. Safronov & Alexander V. Aleksakhin, 2021. "Optimization of Energy-Efficient Functioning of the Oil and Gas Sector of the Economy through Digitalization and Resource Conservation," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 321-330.
    16. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
    17. Nastaran Gholizadeh & Petr Musilek, 2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges," Energies, MDPI, vol. 14(12), pages 1-18, June.
    18. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.
    19. Gema Hernández-Moral & Sofía Mulero-Palencia & Víctor Iván Serna-González & Carla Rodríguez-Alonso & Roberto Sanz-Jimeno & Vangelis Marinakis & Nikos Dimitropoulos & Zoi Mylona & Daniele Antonucci & H, 2021. "Big Data Value Chain: Multiple Perspectives for the Built Environment," Energies, MDPI, vol. 14(15), pages 1-21, July.
    20. Olga Pilipczuk, 2020. "Sustainable Smart Cities and Energy Management: The Labor Market Perspective," Energies, MDPI, vol. 13(22), pages 1-24, November.

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